Is the cup half empty or half full? It seems to me that there are opportunities to get tripped up by even the simplest emissions math, as is the case with the MPG illusion. That complicates negotiations by introducing variations in regions’ perception of fairness, on top of contested value judgments.
Backing up a bit, our role in the climate war game was to provide whatever decision support we could within the time frame of the negotiations. That included generating model runs evaluating the CO2 concentration and temperature consequences of proposed emissions trajectories. It also turned out to be helpful for us to generate a variety of metrics combining population, gdp, and emissions, to support arguments for various equity and burden-sharing allocations. For example, using the game scenario data,
| In 2015’¦ | US | EU | India | China | ROW | World | |
| Population | (millions) | 327 | 512 | 1270 | 1420 | 2581 | 6110 |
| (share) | 5% | 8% | 21% | 23% | 42% | 100% | |
| GDP | (Trillion$) | 16.4 | 17.3 | 5.3 | 14.3 | 32.0 | 85.3 |
| (share) | 19% | 20% | 6% | 17% | 38% | 100% | |
| Emissions | (MTonCO2/yr) | 6,392 | 4,011 | 1,804 | 8,632 | 13,232 | 34,071 |
| (share) | 19% | 12% | 5% | 25% | 39% | 100% | |
| Cumulative Emissions | (GTonCO2) | 372 | 276 | 40 | 163 | 560 | 1410 |
| (share) | 26% | 20% | 3% | 12% | 40% | 100% | |
| (per capita) | 1.14 | 0.54 | 0.03 | 0.11 | 0.22 | 0.23 | |
| Emissions/GDP | (TonCO2 /Million$) | 390 | 232 | 340 | 604 | 414 | 400 |
| Emissions/Cap | (TonCO2 /person/yr) | 19.5 | 7.8 | 1.4 | 6.1 | 5.1 | 5.6 |
| GDP/Cap | ($/person/yr) | 50,153 | 33,789 | 4,173 | 10,070 | 12,391 | 13,957 |
That’s fairly predictable stuff, until you start allocating emissions reductions on the basis of population (per capita equity) or other metrics. For example,
| -80% from 2005 Global Emissions Allocated by’¦ | US | EU | India | China | ROW | World | |
| Population | (MTonCO2/yr) | 285 | 446 | 1,107 | 1,237 | 2,249 | 5,324 |
| Reduction | 96% | 89% | 39% | 86% | 83% | 84% | |
| GDP | (MTonCO2/yr) | 1,024 | 1,080 | 331 | 893 | 1,997 | 5,324 |
| Reduction | 84% | 73% | 82% | 90% | 85% | 84% | |
The population-based reductions above are fair in the sense that every nation winds up with the same per capita emissions. So why does it appear that China, at 86%, is working nearly as hard as the US, at 96%? The answer is that in one sense it’s not. What really matters is not how much you have to cut, but what you get to keep. When emissions are allocated on a uniform per capita basis, the US gets to keep 4% of business-as-usual. China keeps 14%, and India keeps 61% – 3.5 and 15 times as much, respectively.
The big difference in retained emissions translates to a big difference in cost. While early emissions reductions are cheap or even negative-cost, the cost of deep cuts rises steeply, at least until changes in technology and preferences open up new possibilities. If, for example, costs follow Nordhaus’ parameterized function from the original DICE model, they are roughly cubic and it would take 7% of GDP to eliminate all emissions. (The actual equation is cost = 0.0686*GDP*reduction^2.887.) Then using the population-based cuts above, the US would be spending 6.1% of GDP, China 4.4%, and India 0.5%. The US would be spending almost 150 times as much as India on a per-capita basis, but also better able to afford it. Each of these different characterizations of cuts has a quite different “feel”. (Obviously there are many nuances that I’ve ignored here. For example, one can make a plausible argument that uniform per capita emissions are not equitable enough, when one considers cumulative carbon emissions over history. Economies have different sectoral compositions and climatic conditions. Strict population-based cuts would lead to large differences in regional carbon abatement costs, so they ought to be regarded as rights subject to redistribution by trade rather than as physical allocations.)
The perceptual difference between reductions and “retentions” is further complicated by the fact that any discussion of future reductions is inevitably judged against an arbitrary baseline, either a current benchmark subject to historical accident and measurement error, or a future business-as-usual trajectory subject to large forecast error. This leads me to wonder whether a conversation about actions and effort might be more productive than a conversation about targets. The trick is to make those actions enforceable and sufficient.